Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to limited quantity and quality data, non-identifiability causal graph, it almost impossible infer single precise DAG. Some methods approximate posterior distribution DAGs explore DAG space via Markov chain Monte Carlo (MCMC), over nature super-exponential growth, ac...

In discrete undirected graphical models, the conditional independence of node labels Y is specified by the graph structure. We study the case where there is another input random vector X (e.g. observed features) such that the distribution P (Y | X) is determined by functions of X that characterize the (higher-order) interactions among the Y ’s. The main contribution of this paper is to learn th...

the aim of this empirical study is to explore the trade-off model and pecking order model of capital structure. the investigation is performed using panel data procedures for a sample of 76 firms listed in tehran stock exchange during 2007-2010.the study employs ols regression model in examining the capital structure of firms in iran. the study employs variables reflecting differing theoretical...